We analyze how Hierarchical Attention Neural Networks could be helpful with malware detection and classification scenarios, demonstrating the usefulness of this approach for generic sequence intent analysis. The novelty of our approach is in applying techniques that are used to discover structure in a narrative text to data that describes the behavior of executables.
We demonstrate how to train Object Detection models using CNTK and Tensoflow DNN frameworks. Azure ML Workbench is used as the main training and model hosting infrastructure.
An overview of different image classification approaches including Microsoft Azure Custom Vision Service and CNTK for various levels of classification complexity.
We use Deep Learning to turn a painful and time-consuming leak-detection task for water and oil pipelines into a fast, painless process. Using Python and Fast Fourier Transforms, we turn audio sensor data into images, then use Convolutional Neural Networks to detect and classify pipeline anomalies.